Search Results for author: David Suter

Found 38 papers, 7 papers with code

Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation

no code implementations16 Mar 2024 Mariia Khan, Yue Qiu, Yuren Cong, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn

The foundational Segment Anything Model (SAM) is designed for promptable multi-class multi-instance segmentation but tends to output part or sub-part masks in the "everything" mode for various real-world applications.

Instance Segmentation Object +3

SCOL: Supervised Contrastive Ordinal Loss for Abdominal Aortic Calcification Scoring on Vertebral Fracture Assessment Scans

1 code implementation22 Jul 2023 Afsah Saleem, Zaid Ilyas, David Suter, Ghulam Mubashar Hassan, Siobhan Reid, John T. Schousboe, Richard Prince, William D. Leslie, Joshua R. Lewis, Syed Zulqarnain Gilani

We develop a Dual-encoder Contrastive Ordinal Learning (DCOL) framework that learns the contrastive ordinal representation at global and local levels to improve the feature separability and class diversity in latent space among the AAC-24 genera.

regression

Single Domain Generalization via Normalised Cross-correlation Based Convolutions

no code implementations12 Jul 2023 WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, David Suter, Alireza Bab-Hadiashar

This approach is invariant to both affine shifts and changes in energy within a local feature patch and eliminates the need for commonly used non-linear activation functions.

Data Augmentation Domain Generalization

Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration

no code implementations21 Feb 2022 Giang Truong, Huu Le, Alvaro Parra, Syed Zulqarnain Gilani, Syed M. S. Islam, David Suter

The volume of data to handle, and still elusive need to have the registration occur fully reliably and fully automatically, mean there is a need to innovate further.

Point Cloud Registration Semantic Segmentation

A Hybrid Quantum-Classical Algorithm for Robust Fitting

1 code implementation CVPR 2022 Anh-Dzung Doan, Michele Sasdelli, David Suter, Tat-Jun Chin

While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics.

Maximum Consensus by Weighted Influences of Monotone Boolean Functions

no code implementations CVPR 2022 Erchuan Zhang, David Suter, Ruwan Tennakoon, Tat-Jun Chin, Alireza Bab-Hadiashar, Giang Truong, Syed Zulqarnain Gilani

In particular, we study endowing the Boolean cube with the Bernoulli measure and performing biased (as opposed to uniform) sampling.

Event Data Association via Robust Model Fitting for Event-based Object Tracking

no code implementations25 Oct 2021 Haosheng Chen, Shuyuan Lin, David Suter, Yan Yan, Hanzi Wang

Event-based approaches, which are based on bio-inspired asynchronous event cameras, have achieved promising performance on various computer vision tasks.

Model Selection Object Tracking

Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning

no code implementations15 Jun 2021 WeiQin Chuah, Ruwan Tennakoon, Alireza Bab-Hadiashar, David Suter

We provide evidence that demonstrates that learning of features in the synthetic domain by a stereo matching network is heavily influenced by two "shortcuts" presented in the synthetic data: (1) identical local statistics (RGB colour features) between matching pixels in the synthetic stereo images and (2) lack of realism in synthetic textures on 3D objects simulated in game engines.

Depth Estimation Stereo Matching

Consensus Maximisation Using Influences of Monotone Boolean Functions

1 code implementation CVPR 2021 Ruwan Tennakoon, David Suter, Erchuan Zhang, Tat-Jun Chin, Alireza Bab-Hadiashar

Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level.

Adjusting Bias in Long Range Stereo Matching: A semantics guided approach

no code implementations10 Sep 2020 WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad, Alireza Bab-Hadiashar, David Suter

Consequently, the learning algorithms often produce unreliable depth estimates of foreground objects, particularly at large distances~($>50$m).

3D Object Detection Autonomous Navigation +5

Quantum Robust Fitting

no code implementations12 Jun 2020 Tat-Jun Chin, David Suter, Shin-Fang Chng, James Quach

Many computer vision applications need to recover structure from imperfect measurements of the real world.

Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon

no code implementations11 May 2020 David Suter, Ruwan Tennakoon, Erchuan Zhang, Tat-Jun Chin, Alireza Bab-Hadiashar

This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem.

Vocal Bursts Type Prediction

End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking

no code implementations14 Feb 2020 Haosheng Chen, David Suter, Qiangqiang Wu, Hanzi Wang

We feed the sequence of TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform an end-to-end 5-DoF object motion regression.

Motion Estimation Object +2

Hypergraph Optimization for Multi-structural Geometric Model Fitting

no code implementations13 Feb 2020 Shuyuan Lin, Guobao Xiao, Yan Yan, David Suter, Hanzi Wang

Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to represent the complex relationship between data points.

Clustering

Deterministic consensus maximization with biconvex programming

1 code implementation ECCV 2018 Zhipeng Cai, Tat-Jun Chin, Huu Le, David Suter

In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization.

Superpixel-guided Two-view Deterministic Geometric Model Fitting

no code implementations3 May 2018 Guobao Xiao, Hanzi Wang, Yan Yan, David Suter

Specifically, SDF includes three main parts: a deterministic sampling algorithm, a model hypothesis updating strategy and a novel model selection algorithm.

Model Selection Superpixels +1

Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting

no code implementations4 Feb 2018 Hanzi Wang, Guobao Xiao, Yan Yan, David Suter

We cast the task of geometric model fitting as a representative mode-seeking problem on hypergraphs.

Deterministic Approximate Methods for Maximum Consensus Robust Fitting

1 code implementation27 Oct 2017 Huu Le, Tat-Jun Chin, Anders Eriksson, Thanh-Toan Do, David Suter

Further, our approach is naturally applicable to estimation problems with geometric residuals

Quasiconvex Plane Sweep for Triangulation With Outliers

no code implementations ICCV 2017 Qianggong Zhang, Tat-Jun Chin, David Suter

Relative to the random sampling heuristic, our algorithm not only guarantees deterministic convergence to a local minimum, it typically achieves higher quality solutions in similar runtimes.

An Exact Penalty Method for Locally Convergent Maximum Consensus

no code implementations CVPR 2017 Huu Le, Tat-Jun Chin, David Suter

Our method is based on a formulating the problem with linear complementarity constraints, then defining a penalized version which is provably equivalent to the original problem.

Clustering with Hypergraphs: The Case for Large Hyperedges

no code implementations IEEE Transactions on Pattern Analysis and Machine Intelligence 2016 Pulak Purkait, Tat-Jun Chin, Hanno Ackermann, David Suter

The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision.

Clustering Face Clustering +1

Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data

no code implementations20 Jul 2016 Guobao Xiao, Hanzi Wang, Yan Yan, David Suter

The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods.

Model Selection Superpixels +1

Hypergraph Modelling for Geometric Model Fitting

no code implementations11 Jul 2016 Guobao Xiao, Hanzi Wang, Taotao Lai, David Suter

The hypergraph, with large and "data-determined" degrees of hyperedges, can express the complex relationships between model hypotheses and data points.

Conformal Surface Alignment With Optimal Mobius Search

no code implementations CVPR 2016 Huu Le, Tat-Jun Chin, David Suter

Deformations of surfaces with the same intrinsic shape can often be described accurately by a conformal model.

Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting

no code implementations ICCV 2015 Hanzi Wang, Guobao Xiao, Yan Yan, David Suter

In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a weight-aware sampling technique.

Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition

no code implementations24 Mar 2016 Yan Yan, Hanzi Wang, David Suter

In this paper, we propose an effective feature extraction algorithm, called Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face recognition.

Face Recognition Robust Face Recognition

Efficient Globally Optimal Consensus Maximisation With Tree Search

no code implementations CVPR 2015 Tat-Jun Chin, Pulak Purkait, Anders Eriksson, David Suter

We aim to change this state of affairs by proposing a very efficient algorithm for global maximisation of consensus.

Fast Supervised Hashing with Decision Trees for High-Dimensional Data

1 code implementation CVPR 2014 Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton Van Den Hengel, David Suter

Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data.

Retrieval Vocal Bursts Intensity Prediction

Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization

no code implementations23 Nov 2013 Guosheng Lin, Chunhua Shen, Anton Van Den Hengel, David Suter

Different from most existing multi-class boosting methods, which use the same set of weak learners for all the classes, we train class specified weak learners (i. e., each class has a different set of weak learners).

Multi-class Classification

A General Two-Step Approach to Learning-Based Hashing

no code implementations7 Sep 2013 Guosheng Lin, Chunhua Shen, David Suter, Anton Van Den Hengel

This framework allows a number of existing approaches to hashing to be placed in context, and simplifies the development of new problem-specific hashing methods.

Vocal Bursts Valence Prediction

As-Projective-As-Possible Image Stitching with Moving DLT

no code implementations CVPR 2013 Julio Zaragoza, Tat-Jun Chin, Michael S. Brown, David Suter

We investigate projective estimation under model inadequacies, i. e., when the underpinning assumptions of the projective model are not fully satisfied by the data.

Image Stitching

Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC

no code implementations NeurIPS 2011 Trung T. Pham, Tat-Jun Chin, Jin Yu, David Suter

Multi-structure model fitting has traditionally taken a two-stage approach: First, sample a (large) number of model hypotheses, then select the subset of hypotheses that optimise a joint fitting and model selection criterion.

Computational Efficiency Model Selection

The Ordered Residual Kernel for Robust Motion Subspace Clustering

no code implementations NeurIPS 2009 Tat-Jun Chin, Hanzi Wang, David Suter

The kernel permits the application of well-established statistical learning methods for effective outlier rejection, automatic recovery of the number of motions and accurate segmentation of the point trajectories.

Clustering Computational Efficiency +2

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